Compact aero-thermo model base point linear system based state estimator

ABSTRACT

Systems and methods for controlling a fluid-based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The model processor may further include an estimate state module for determining an estimated state of the model based on at least one of a prior state, current state derivatives, solver state errors, and synthesized parameters. The estimate state module determines an estimator gain associated with the current state derivatives and applies the estimator gain to determine the estimated state of the model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. application Ser. No. 14/767,995 filed Aug. 14, 2015, which is a US National Stage under 35 USC § 371 of International Patent Application No. PCT/US14/27877 filed on Mar. 14, 2014, and claims priority under 35 USC § 119(e) to U.S. Provisional Patent Application No. 61/800,440 filed on Mar. 15, 2013, the disclosures of which are incorporated by reference herein in their entirety.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates to the design and control of engineering systems, and more particularly, to design and control of fluid-based engineering systems.

BACKGROUND

Fluid-based engineering systems are widely used and may include gas turbine engines for aviation and power generation, HVAC&R (heating, ventilation, air-conditioning and refrigeration), fuel cells, and other, more generalized fluid processing systems for hydrocarbon extraction, materials processing, and manufacture. These systems may contain any or all of the following components: turbo-machinery, fuel cell stacks, electric motors, pipes, ducts, valves, mixers, nozzles, heat exchangers, gears, chemical apparatuses and other devices for generating or modifying a fluid flow.

Each of these applications places different operational demands on the engineering control system. In gas turbine engine applications, for example, the relevant cycle is typically a Brayton turbine or first Ericsson cycle, and the basic thermodynamic parameters (or process variables) are the pressure, temperature and flow rate of the working fluid at the inlet, compressor, combustor, turbine, and exhaust. The parameters may be related to the overall thrust, rotational energy, or other measure of power output. In order to precisely control this output while maintaining safe, reliable and efficient engine operation, the engineering control system must be fast, accurate, robust, and provide real-time control capability across all required performance levels. While the relevant process variables vary depending on the system type and configuration, the need for precise, efficient and reliable engineering control remains the same, as do the economic constraints on overall cost and operational/maintenance requirements.

Further, because direct measurements of system parameters controlled may not be possible (due to undeveloped technology, prohibitive cost, unreliable equipment, etc.), the control system may require real time estimation of system parameters. System parameters may be mathematical abstractions of engineering systems and/or process for a given set of measured inputs used as control feedback.

In the past, control systems for such fluid-based engineering systems relied on piecewise linear state variable representations. These control systems, by their nature, were limited to relatively simple non-linear systems. Another approach used in the past relies on semi-empirical relationships that tie important system parameters to control sensors; the drawback of such a system is that it may lack accuracy and is expensive due to the additional hardware required for implementation. Other attempts have been made to deploy stationary simulations in a retail environment; however, by their nature, these models are large, use iterative solvers, have high maintenance cost and lack robustness critical in a real time environment.

A known approach to modern fluid-based engineering system control is the use of component level physics based non-iterative mathematical abstractions of fluid-based engineering systems. These mathematical abstractions are conceptualized in a software environment specific to the applied fluid-based engineering system. Such example systems and methods for engineering system control are further detailed in U.S. Pat. No. 8,131,384 (“Design and Control of Engineering Systems Utilizing Component-Level Dynamic Mathematical Model With Multiple-Input Multiple-Output Estimator”), which is hereby incorporated by reference.

A need exists for an engine parameter on-board synthesis (EPOS) in the real-time control system of a fluid based engineering system that may overcome the computational inefficiencies of prior EPOS models.

BRIEF DESCRIPTION

In accordance with an aspect of the disclosure, a control system is disclosed. The control system may include an actuator for positioning a control surface of a control device. The control system may include a control law for controlling the actuator based on a model output. The control system may include a model processor for generating the model output, the model processor including an input object for processing a model input vector and setting a model operating mode. A set state module is included for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode, where the open loop model generates current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector. A constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and including a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state, the current state derivatives, the solver state errors, and the synthesized parameters. The estimate state module determines an estimator gain associated with the current state derivatives and applies the estimator gain to determine the estimated state of the model. The model processor may include an output object for processing at least the synthesized parameters of the model to determine the model output.

In a refinement, determining the estimator gain may include employing multidimensional gain base point interpolation.

In a further refinement, the multidimensional gain base point interpolation may include using at least three scheduling parameters.

In a refinement, determining the estimator gain associated with the current state derivatives and applying the estimator gain may be achieved by using a fixed structure with a configurable architecture.

In a further refinement, the configurable architecture may include a first leg for scaling and correcting an error input using scaling and correcting factors.

In a further refinement, the configurable architecture may further include a second leg for selecting groups of vectors from the error input for gain determination and a third leg for determining and applying gain to the groups of vectors.

In a further refinement, the configurable architecture may further include a fourth leg for unscaling and uncorrecting the groups of vectors.

In a further refinement, the configurable architecture may further include a fifth leg for assigning values to output vectors of the estimated state of the model.

In a refinement, the control device may be a gas turbine engine.

In accordance with another aspect of the disclosure, a method for controlling a control device is disclosed. The method may include generating a model output using a model processor. The model processor can include an input object for processing a model input vector and setting a model operating mode, a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The open loop model can generate current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector, where a constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and including a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component. The model processor may further include an estimate state module for determining an estimated state of the model based on at least one of a prior state model, the current state derivatives, the solver state errors, and the synthesized parameters. The estimate state module can determine an estimator gain associated with the current state derivatives and apply the estimator gain to determine the estimated state of the model. The model processor may include an output object for processing at least the synthesized parameters of the model to determine the model output. The method may further include controlling an actuator associated with the control device as a function of a model output using a control law and positioning a control surface of the control device using the actuator.

In a refinement, determining the estimator gain may include employing multidimensional gain base point interpolation.

In a further refinement, the multidimensional gain base point interpolation may include using at least three scheduling parameters.

In a refinement, determining estimator gain associated with the current state derivatives and applying the estimator gain may be achieved by using a fixed structure with a configurable architecture.

In a further refinement, the configurable architecture may include a first leg for scaling and correcting an error input using scaling and correcting factors.

In a further refinement, the configurable architecture may further include a second leg for selecting groups of vectors from the error input for gain determination and a third leg for determining and applying gain to the groups of vectors.

In accordance with another aspect of the disclosure, a gas turbine engine is disclosed. The gas turbine engine may include a fan, a compressor section downstream of the fan, and a combustor section downstream of the compressor section, and a turbine section downstream of the compressor section. Further, the gas turbine engine may include an actuator for positioning a control surface of the gas turbine engine. The gas turbine engine may include a control law for controlling the actuator as a function of a model output. The gas turbine engine may include a model processor for generating the model output, the model processor including an input object for processing a model input vector and setting a model operating mode. A set state module can be included for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode. The open loop model generates current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector, where a constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the gas turbine engine and including a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component. The model processor may further include an estimate state module for determining an estimated state of the model based on at least one of a prior state, the current state derivatives, the solver state errors, and the synthesized parameters, the estimate state module determining an estimator gain associated with the current state derivatives and applying the estimator gain to determine the estimated state of the model. The model processor may include an output object for processing at least the synthesized parameters of the model to determine the model output.

In a refinement, determining the estimator gain may include employing multidimensional gain base point interpolation.

In a further refinement, the multidimensional gain base point interpolation may include using at least three scheduling parameters.

In a refinement, determining estimator gain associated with the current state derivatives and applying the estimator gain may be achieved by using a fixed structure with a configurable architecture.

In a further refinement, the configurable architecture may include a first leg for selecting groups of vectors from the error input for gain determination and a second leg for determining and applying gain to the groups of vectors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary control system for a fluid-based engineering system.

FIG. 2 is a block diagram of an exemplary engine parameter on-board synthesis (EPOS) module of the control system of FIG. 1.

FIG. 3 is a block diagram of an exemplary CAM input object of the EPOS of FIG. 2.

FIG. 4 is a block diagram of an exemplary compact aero-thermal (CAM) object of the EPOS of FIG. 2.

FIG. 5 is a block diagram of an exemplary open-loop model of the CAM object of FIG. 4.

FIG. 6 is a block diagram of an exemplary output conditioning module of the EPOS of FIG. 2.

FIG. 7 is a block diagram of an exemplary estimate state module of the CAM object of FIG. 4.

FIG. 8 is a plot of scheduling parameters, illustrating gain design processes for the estimate state module of FIG. 7.

FIG. 9 is a block diagram of an alternative exemplary estimate state module of the CAM object of FIG. 4.

FIG. 10 is a flowchart representative of machine readable instructions that may be executed to implement the example EPOS of FIGS. 1 and/or 2.

FIG. 11 is a flowchart representative of machine readable instructions that may be executed to implement the example CAM object of FIGS. 2 and/or 4.

FIG. 12 is a block diagram of an example processing system that may execute the example machine readable instructions of FIGS. 7-8 and/or any elements of the present disclosure herein.

It should be understood that the drawings are not necessarily to scale. In certain instances, details which are not necessary for an understanding of this disclosure or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the particular embodiments illustrated herein.

DETAILED DESCRIPTION

Referring to the drawings and with specific reference to FIG. 1, a control system for a fluid-based engineering system in accordance with the present disclosure is generally referred to by reference numeral 100. Control demands may be generated by an operator interface 140 and may be received by the engine parameter on board synthesis (EPOS) 110. For example, the operator interface 140 may be a real-time interface such as a cockpit navigation system and/or an operator workstation. Additionally or alternatively, the operator interface 140 may include another, more generalized process control interface, which is suitable for logging control commands to software control components 150, including, for example, a guidance, navigation, and control computer or autopilot system(s). Further, control demands may be generated by an internal memory or any other internal programming operatively associated with software control elements 150.

The control elements 150 may include EPOS 110 and a control law 111 that may generate and/or process control instructions for the apparatus 130. The EPOS 110 and control law 111 may be implemented as software modules designed to monitor, control, or otherwise act in associative function with regards to an apparatus 130. The control law 111 may obtain control feedbacks from the EPOS 110 and control commands from the operator interface 140. The control law 111 may generate control requests in engineering units to be processed by hardware control elements 120 to control the apparatus 130.

Additionally, the software control components 150 may include an output conditioner 113 and/or an input conditioner 115 to process output and/or input data for the data's respective input/output destination. The input to the EPOS 110, provided by the input conditioner 115, may be processed by fault detection and accommodation (FDA) logic 117 to detect range faults as well as in-range failures (e.g., rate-limit, cross-channel mismatch, etc.) and provide a reasonable input value along with a health status indication for the input.

Further, the hardware control components 120 may convert digital data generated by the software control components 150 to an analog form readable by the apparatus 130 (e.g., electrical signals), convert analog data generated by the apparatus 130 into digital data readable software components 150, condition such input and output data for readability, and/or control actuators 124 associated with the apparatus 130. The digital-to-analog convertor 122 can transform digital signals generated by the control law 111 into actuator requests. The actuators 124 may be one or more devices which use control hardware to position various control components of the apparatus 130 in accordance with instructions generated by the EPOS 110. Actuators, such as the actuators 124, may be designed to provide quick and accurate control of an apparatus.

Actuator sensors 125 may be included to measure various states of the actuators 124, wherein the actuator states (or positions) may be related to the physical configuration of the various control components of the apparatus 130. For example, fluid-based systems often include actuators whose linear or angular positions are sensed by actuator sensors 124, and which are related to the physical position of control surfaces or other control devices located proximate to a compressor, combustor, turbine and/or nozzle/exhaust assembly.

Further, the hardware control components 120 may include apparatus system sensors 126. The apparatus system sensors 126 may measure operational parameters associated with the apparatus 130. For example, fluid-based systems may include apparatus system sensors 126 that measure the working fluid pressure, temperature and fluid flow at various axial and radial locations in the flow path. Apparatus system sensors 126 may comprise a variety of different sensing devices, including, but not limited to, temperature sensors, flow sensors, vibration sensors, debris sensors, current sensors, voltage sensors, level sensors, altitude sensors and/or blade tip sensors. Apparatus system sensors 126 may be positioned to measure operational parameters related to the function of apparatus 130, e.g., parameters related to control commands submitted to EPOS 110 and control requests generated by EPOS 110 in order to direct actuators 124 to control apparatus 130.

Both the apparatus system sensors 126 and the actuator sensors 125 may produce electrical signals based upon a read-out result from said sensors. The electrical signals produced by the actuator sensors 125 and the apparatus system sensors 126 may be transmitted to an analog-to-digital convertor 123. The analog-to-digital convertor may convert the electrical signals into digital signal data which may be compatible with and read by the EPOS 110 after processing by the input conditioning module 115.

The apparatus 130 may be any fluid-based engineering system. Example fluid-based engineering systems may include gas turbine engines for aviation and power generation, HVAC&R (heating, ventilation, air-conditioning and refrigeration), fuel cells, and other, more generalized fluid processing systems for hydrocarbon extraction, materials processing, and manufacture. In various embodiments, the physical components of apparatus 130 include, but are not limited to including, compressors, combustors, turbines, shafts, spools, fans, blowers, heat exchangers, burners, fuel cells, electric motors and generators, reactor vessels, storage vessels, fluid separators, pipes, ducts, valves, mixers and other fluid processing or flow control devices.

In some examples, the apparatus 130 may perform a thermodynamic cycle on a working fluid in order to generate rotational energy, electrical power or reactive trust, to provide heating, ventilation, air conditioning and refrigeration, or to perform other fluid processing functions. The range of available cycles includes, but is not limited to, the following cycles and their derivatives: Otto cycles, Diesel cycles, Brayton turbine (or first Ericsson) cycles, Brayton jet (Barber/Joule) cycles, Bell-Coleman (reverse Brayton) cycles, Ericsson (Second Ericsson) cycles, Lenoir (pulse-jet) cycles, and Carnot, Stoddard and Stirling cycles. Additionally or alternatively, apparatus 130 may perform a number of individual thermodynamic processes for heating, cooling, flow control, or for processing applications in agriculture, transportation, food and beverage production, pharmaceutical production, or manufacturing, or for the extraction, transportation or processing of a hydrocarbon fuel. The range of available thermodynamic processes includes, but is not limited to, adiabatic, isothermal, isobaric, isentropic, and isometric (isochoric or isovolumetric) transformations, exothermic reactions, endothermic reactions and phase changes.

In the present example, the apparatus 130 is a gas turbine engine. As such, the monitored aspects of the apparatus 130 may include, but are not limited to, a compressor, combustor, turbine and/or nozzle/exhaust assembly. In an application for a gas turbine engine, the input and output values received/generated by the EPOS 110 may be vectors representing values for positions (i.e., nozzle areas, variable vane angles, flow path areas, etc.), states, and actual sensed values of parameters (i.e., spool speeds, gas path temperatures, pressures proximate to components, flow rates proximate to components, etc.) related to the components of a gas turbine engine (i.e., a compressor, combustor, turbine and/or nozzle/exhaust assembly, etc.).

The data processed by the EPOS 110 are vectors containing parameters related to functions of the apparatus 130. Example input vectors for the EPOS 110 may include an external inputs vector (U_(E)) and a corrector truth vector (Y_(Ct)). U_(E) may contain values for external inputs to be processed by the EPOS 110. U_(E) may describe the configurations, positions and states of various control elements in the apparatus 130. In a gas turbine engine, for example, individual elements of external inputs vector U_(E) may have a set of values related to effector position; these effector position values may describe fuel flow rates, nozzle areas, variable vane angles, flow path orifice areas, and other control element parameters. Further, U_(E) may have a set of values related to boundary conditions related to the operation of apparatus 130. Some boundary conditions may be directly measured by apparatus system sensors 126, such as fluid temperatures, pressures and flow rates at physical boundaries of apparatus 130. In fluid-based applications, the boundary conditions may include boundary flow conditions and inlet and outlet locations. Other boundary conditions specific to aircraft applications include, but are not limited to, flight velocity, altitude, and bleed or power extractions parameters.

The corrector truth vector Y_(Ct) may contain data associated with real time execution of the control system and describe the actual (sensed) values of the parameters related to the operation of apparatus 130. The elements of Y_(Ct) may be based on measurements taken by the actuator sensors 125 and/or the apparatus system sensors 126. Further, elements of Y_(Ct) may be based on values derived from a well-understood and trusted model of sensed parameters; for example, a flow rate model based on a differential pressure drop across a Pitot tube or Venturi tube. For gas turbine engines, typical Y_(Ct) vector elements include, but are not limited to, spool speeds, gas path temperatures, and/or pressures all values of which may be proximate to engine components such as compressors, combustors, and turbines. In the context of non-real time applications, including calibration, Y_(Ct) may correspond to high fidelity data which can either be physically tested or model-based.

Employing the compact aero-thermal model (CAM) of the present disclosure, FIG. 2 illustrates an embodiment of the EPOS 110 of FIG. 1 in further detail. The EPOS 110 of FIG. 2 may include, but is not limited to including a CAM input object 220, a compact aero-thermal model (CAM) object 230, and a CAM output object 240. The EPOS 110 may receive raw input data related to vectors U_(ERaw) and Y_(CtRaw). U_(ERaw) may contain values obtained from the actuator sensors 125, the apparatus system sensors 126 and/or any other associated sensors and/or inputs. Y_(CtRaw) may contain values obtained from the actuator sensors 125, the apparatus system sensors 126 and/or any other associated sensors and/or inputs.

The CAM input object 220 may package selected values from the received input U_(E) vector into an input vector U_(E_in). Similarly, the CAM input object 220 may package selected values from the received input into an input corrector truth vector, Y_(Ct_in). Further, the vectors U_(E_in) and Y_(Ct_in) may then be conditioned to protect the input values; this may be done by range limiting the values, by constraining the values based on instructions, and/or by performing any additional input modifications function on the vectors. The CAM input object 220 may also use the input vectors received to determine an operating mode (OpMode) for the FADEC 110. The input conditioning module 220 may output a conditioned external input vector U_(E), a conditioned truth vector Y_(Ct), and an OpMode vector.

The input validity of the vector values may be ensured by fault detection and accommodation logic (e.g., through processing from the FDA logic 117) specific to each sensor input of the CAM input object 220. The fault detection and accommodation logic detects range faults as well as in-range failures (i.e., rate-limit, cross-channel mismatch, etc.) and provides a reasonable value in all cases along with a health status indication. An example CAM input object 220 is described in greater detail below with reference to FIG. 3.

The output of CAM input object 220 is received by the CAM object 230. The CAM object 230 may contain aero-thermal representations, or component modules, of engine components. The component modules within the CAM object 230 may operate according to the system's constraints related to mathematical abstractions of physical laws that govern behavior of the apparatus 130 (i.e., laws of conservation of energy, conservation of mass, conservation of momentum, Newton's 2^(nd) law for rotating systems, and/or any additional known calculable physics model). The system constraints for each contained module within the CAM object 230 may have specific constraints programmed within to simulate a monitored area and/or function of the apparatus 130 (i.e., a bypass duct bleeds module, a low spool compressor module, a burner module, a parasitic power extraction module, etc.).

The CAM object 230 may use the input vectors along with internal solver states, representing on-board corrector states, solver states, and physics states, while functioning. The solver states may be introduced to address fast dynamics, resolve algebraic loops and smooth highly non-linear model elements. The CAM object 230 may output a synthesized parameters vector Y. The vector Y may be estimated in relation to the operating range determined by the CAM object 230. An example CAM object 230 is described in greater detail below with reference to FIG. 4.

The output of the CAM object 230 may be received by the CAM output object 240. The CAM output object 240 may post-process select CAM outputs that are needed by consuming control software and/or hardware. For some outputs, the CAM output object 240 may perform a unit conversion, may apply a test adder, and/or may perform interpolation between ambient conditions and/or CAM output during starting operation. The CAM output object 240 may unpack the Y vector into values specific values desired by related components (i.e., temperatures, pressures, flows, sensor temperatures, and/or other output synthesis). The CAM output object 240 may also output inter-component station flows, temperatures, pressures, and/or fuel to air ratios, torque, thrust, bleed flows, and/or compressor and turbine case clearances. The CAM output object 240 may also indicate the current status (e.g., the aforementioned “OpMode” operating mode) as determined by the EPOS 110. An example CAM output object 240 is described in greater detail below with reference to FIG. 10.

Returning to the CAM input object 220, FIG. 3 illustrates an exemplary embodiment of the CAM input object 220 of FIG. 2. The CAM input object of FIG. 3 may include a U_(E) vector packager 310, a Y_(Ct) vector packager 320, an OpMode Determiner 330, and an input protection module 340. The U_(E) vector packager may receive vector U_(ERaw) and the U_(E) vector packager 310 may select the desired values from the input vectors and may create vector U_(E) in, which may be output to the input protection module 340. The U_(E) vector packager 310 may also be used for unit conversion and for synthesizing values for U_(E) which may not be present. Similarly, the Y_(Ct) vector packager may receive vector Y_(CtRaw). The Y_(Ct) vector packager 310 may select the desired values from the input vectors and may create vector Y_(Ct_in), which may be output to the input protection module 340.

The OpMode determiner 330 may establish the operating mode of the CAM object 230 based on the health status of input values that are necessary to run in each operating mode. The health status may include status of control sensors determined by the FDA logic 117, as well as internally generated information by the CAM object 230, such as conditions of internal states and outputs. The OpMode determiner 330 may operate using a logic design that seeks the highest fidelity mode based on available inputs and may fall back to decreased fidelity modes to accommodate faults. The operating mode determined by the OpMode determiner 330 is one of a programmed list of operating modes related to the function of the apparatus 130 and based on the input vector values and/or the condition of CAM states and/or outputs. The functions of various downstream elements may be affected by the resulting operating mode determined by the OpMode determiner 330.

Once the OpMode of the CAM is determined, the input protection module 340 uses the OpMode vector and the input from the vector packagers 310, 320 to determine input vectors U_(E) and Y_(Ct). Said vectors are received by the CAM object 230, illustrated in greater detail in FIG. 4.

The CAM object 230 may generate state vectors X_(C), X_(S), and X_(P). The physics state vector (X_(P)) contains simulated parameters related to dynamics of the apparatus 130 during a time of interested, whose derivatives (X_(pDot)) are calculated in an open loop model 410. The vector X_(P) may include, but is not limited to including, spool shaft speeds, apparatus material temperatures, etc. The solver state vector X_(S) may contain values related to adjustments that are made to certain components of the apparatus 130. These values may be adjustments to make up for errors coming from the CAM. The X_(C) vector may contain values related to on-board corrector states, which are simulated component level values that are a refinement of the Y_(Ct) vector to make the modeled Y_(C) vector comparable to the real values of Y_(Ct). Further, because the CAM object 230 may be implemented as a discrete real time simulation, the CAM object 230 may fully execute each dynamic pass of the simulation, each pass being denoted by letter k. The product of each simulation pass k and simulation time step dt is the simulation time. The values of k may increase sequentially by 1, (e.g. k=[1, 2, 3, . . . ]).

The set state module 420 may receive input of the U_(E), Y_(Ct), and OpMode vectors. Additionally, the set state module 420 may receive the prior state's values of the X vectors generated by the estimate state module 440. In FIG. 4, the prior state is denoted by the state “(k−1).” The set state module 420 may override the states in the X vectors with a base point value; however, if the values do not need an override, the set state module 420 may act as a pass through element. The override functions of the set state module 420 may override the values within the X vectors with base-point (U) or external (Y_(Ct)) values depending on the operating mode (OpMode) input to the CAM Module 230. To obtain override values, the set state module 420 may look up base-point values for CAM states for use during initialization. The set state module 420 may also select an active subset of solver states. As output, the set state module 420 may generate corrector state (X_(C)), solver state (X_(S)), and physics state (X_(P)) vectors for use in the open loop model 410.

The open loop model 410 may consist of one or more cycle synthesis modules, each cycle synthesis module relating to a component, function, and/or condition associated with the apparatus 130. In the present example, the open loop model 410 is composed of a variety of cycle synthesis modules that may represent components, functions and/or conditions associated with a cycle of apparatus 130. The open loop module is not limited to any specific number of modules and may contain any number of modules that are used to simulate components, functions, and/or conditions associated with apparatus 130. The open loop model 410 may receive input of the corrector state (X_(C)), solver state (X_(S)), and physics state (X_(P)) vectors from the set state module 420 and the effector/boundary condition vector (U_(E)). The values input to the open loop model 410 may be used as input to the various modules simulating the components of apparatus 130. The open loop model 410 uses the values generated by the cycle synthesis modules to form a synthesized parameters vector Y(k) based on U_(E)(k) and X(k). The synthesized parameters vector Y(k) contains synthesized cycle values determined from the simulated physics of the open loop module 410 and may be used for control of the apparatus 130.

Showing the series of cycle synthesis modules, FIG. 5 illustrates an example embodiment of open loop model 410 of FIG. 4. The open loop model 410 may include a group of primary stream modules 510, a group of secondary stream modules 520, a group of additional modules 530 and a vector data packager 540. The open loop model 410 may receive input of the corrector state (X_(C)), solver state (X_(S)), and physics state (X_(P)) vectors, from the set state module 420, and the effector vector (U_(E)). The group of primary stream modules 510, the group of secondary stream modules 520, and the additional modules 530 all may receive input from the corrector state (X_(C)), solver state (X_(S)), and physics state (X_(P)) vectors from the set state module 420 and the effector vector (U_(E)). Further, the open loop module 410 is not limited to including the above mentioned groups of modules, but rather, the open loop module 410 may omit groups of modules and/or include other groups of modules. Any and all modules of the open loop model 410 may interact with one another to produce the output of each module.

Each module of the open loop module 410 may represent a component of the apparatus 130 and may be implemented by a library of utilities, wherein each utility may be a mathematical representation of the physical properties that make up various parts of component calculations. For example, utilities of the modules may include representations of compressors, turbines, bleeds, pressure losses, etc. that may be reusable throughout the CAM object 230 and may improve readability and maintainability of the EPOS 110. These components may be built from physics representations of aerodynamic and thermodynamic processes. Each module may produce, for example, an output vector containing, for example, total pressure, total temperature, fuel/air ratio, and gas flow at the exit of the component, and/or any other parameter associated with the modeled portion of the apparatus 130.

In some examples, the primary stream modules 510 may include, but are not limited to including, the following based on corresponding elements of the apparatus 130: a CMP_L module 605 modeling a low spool compressor, a D_BLD_STB 610 module modeling a bleed associated with a low spool compressor, a D_CS_INT module 615 modeling a pressure loss associated with air flow passing through a duct of a compressor, a CMP_H module 620 modeling a high spool compressor, a D_I030 625 module modeling a pressure loss associated with instrumentation in a duct at the exit of a high spool compressor, a D_DIF_BURN module 630 modeling a diffuser, a BRN_PRI module 635 modeling a burner, a TRB_H module 640 modeling a high spool turbine, a TRB_L module 645 modeling a low spool turbine, a D_EGV_LT module 650 modeling a low turbine exit guide vane duct, a D_I0495 module 655 modeling pressure losses associated with probes aft of an exit guide vane duct, a D_I_NOZ_PRI module 660 modeling pressure losses related to air moving through a duct, the duct containing instrumentation probes, a D_TEC_NOZ module 665 modeling a primary nozzle duct, a D_NOZ_PRI module 670 modeling pressure losses associated with the primary nozzle duct, and a NOZ_PRI module 675 a primary nozzle. Additionally, the secondary stream modules 520 may include, but are not limited to including, the following based on corresponding elements of the apparatus 130: an example CMP_F_SEC module 705 modeling a fan outer diameter compressor, a D_EGV_FO module 710 modeling a fan exit guide vane duct, a D_BLD_SEC module 715 modeling an exit duct between a low compressor and a high compressor, a D_AVE_140 module 720 modeling a duct downstream of a B25 bleed, a D_BLD_NOZ_SEC module 725 modeling bypass duct bleeds of the apparatus 130, a D_I_NOZ_SEC module 730 modeling a secondary nozzle duct, and a NOZ_SEC module 735 modeling a secondary nozzle. Further, there are several modules that are not associated with a particular stream, the modules 530 may include, but are not limited to including, the following based on conditions of the apparatus 130: a POWER_EXTRACT module 805 modeling effects of energy and/or efficiency losses of the apparatus 130, a FAN_ID_POWER module 810 modeling an accounting for power loss from a fan gear box of the apparatus 130, and a TORQUE_BALANCE module 815 modeling an accounting for conservation of energy related to unsteady torque balance within the apparatus 130, and a CALC_ERR_SLVR module 820 that forms solutions to errors detected in the OLM 410. Further, any additional modules and/or groups of modules modeling any additional physical properties associated with the apparatus 130 may be included as part of the open loop model 410.

The example modules of FIG. 5 may be designed using one or more physics-based configurable utilities. The configurable utilities may be contained in a library of subsystems within the EPOS structure. The open loop module 440 may compile the above mentioned modules from the subsystems of physics-based configurable utilities based on preprogrammed instructions and/or a user input.

Once data is processed throughout various modules of the open loop model 410, the vector data packager 540 receives input data from the group of primary stream modules 510, the group of secondary stream modules 520, and the additional modules 530. In addition to the synthesized parameters vector Y(k) based on the model state and input, the open loop model may also output a solver errors vector (ErrSlvr) related to the solver states vector (X_(S)). Also, the open loop model 410 may collect and output a physics state derivatives vector (X_(PDot)). The received data may be packaged by the vector data packager 540 in the form of vectors Y(k), ErrSlvr, and X_(PDot). Further, the vector data packager may package vector data into fewer vectors and/or additional vectors.

Certain utilities used to comprise said modules of the open loop model 410 may model gas-based properties and may represent, for example, specific heat as a function of temperature and fuel/air ratio, relative pressure as a function of enthalpy and fuel/air ratio, enthalpy as a function of temperature and fuel/air ratio, specific heat ratio as a function of temperature and fuel/air ratio, relative pressure as a function of temperature and fuel/air ratio, relative pressure as a function of temperature and fuel/air ratio, temperature as a function of enthalpy and fuel/air ratio, and/or gas constant and specific heat ratio as a function of temperature and fuel/air ratio. Other example utilities may model thermal conductivity as a function of gas total temperature, the absolute viscosity as a function of gas total temperature, critical flow parameters as a function of specific heat and a gas constant, coefficients of thermal expansion as a function of material temperature and/or type, material specific heat as a function of material temperature and/or type, and/or material thermal conductivity as a function of material temperature and type. Further, other utilities modeling other gas related functions may be present. Additionally or alternatively, any other utilities modeling any other properties associated with the apparatus 130 may be included.

Additionally, the modules comprising the open loop model 410 may include one or more configurable utilities. The configurable utilities may be complex representations of engine components. For example, a configurable utility may represent a particular physical effect in major engine components such as a compressor or a turbine. Each instance of a configurable utility may be selected to be one of several representations of the physical processes it models, even though the interface of the configurable model remains unchanged. Configurable utilities may reconfigure themselves to represent a particular component by switching the underlying configurable subsystems. Using such configurable utilities may benefit the maintainability of a software application of the open loop model 430.

In an example embodiment, a configurable utility may be designed to model a Reynolds effect in the compressors of the apparatus 130 and may reconfigure itself to represent a particular component of the compressor (e.g., a high spool compressor, a low spool compressor, etc.). This example configurable, compressor Reynolds effect utility may be used in formation of cycle synthesis modules and may be used in modules simulating a low spool compressor (e.g., the CMP_L module 605 of FIG. 5), a high spool compressor (e.g., the CMP_H module 620 of FIG. 5), and/or any other module associated with a compressor simulation. Similarly, specific modules of the open loop model 410 may utilize a configurable utility to model Reynolds effect in the turbines of the apparatus 130 and may reconfigure itself to represent a particular component of the compressor.

In an example OLM 410, a specific utility may model physical processes of a compressor of the apparatus 130 and may include representations of such physical processes as sub-utilities. Sub-utilities of an example compressor utility may include, but is not limited to including, basic physics utilities associated with the basic physics of the apparatus 130 (e.g., isentropic compression, laws of thermodynamics, ideal gas properties, etc.), a component aero-thermal map evaluation, gas-material heat transfer properties, models of component bleeds, torque components from adiabatic steady-state, and/or effects of scaling between map and cycle conditions (design, gas property, Reynolds effect, clearance, untwisting effects, etc.). The output of such a compressor utility may include, but is not limited to including, component exit gas flow conditions, bleed flows, swirl angles, total component inlet gas flows, torque extracted, and derivatives of material temperatures. A compressor utility may be operatively associated with other utilities (e.g., off-board correction look up tables with selectable scheduling parameters, a selector for enabling on-board and/or off-board correction for the component, etc.) to form a compressor-related module like, for example, the CMP_L module 605 and/or the CMP_H module 620.

Another example of an OLM model utility is the turbine utility. Sub-utilities of an example turbine utility may include, but are not limited to including, basic physics utilities associated with the basic physics of the apparatus 130 (e.g., isentropic expansion, laws of thermodynamics, ideal gas properties, etc.), a component aero-thermal map evaluation, gas-material heat transfer properties, models of inlet guide vanes, models of consolidated turbine cooling bleeds, rotor inlet temperature calculation, turbine clearance effects, and/or effects of scaling between map and cycle conditions (design, gas property, Reynolds effect, clearance, untwisting effects, etc.). Outputs of such an example turbine utilities may include, but are not limited to including, component exit gas conditions, rotor inlet temperature, flows into the turbine, generated torque, material temperature derivatives, steady state material temperature at current conditions, material temperature time constants, radius of apparatus from thermal expansion, and/or clearance values. A turbine utility may be operatively associated with other utilities (e.g., off-board correction look up tables with selectable scheduling parameters, a selector for enabling on-board and/or off-board correction for the component, etc.) to form a compressor-related module like, for example, the TRB_H module 640 and/or the TRB_L module 645.

Returning to FIG. 4, the sensing synthesis module 430 may model control sensor measurements that differ from corresponding average gas path engine station estimates packaged in Y due to regime/location effects in the sensor surroundings and sensor body thermal inertia. Receiving input of Y and the derivative physics state vector (X_(PDot)), the sensing synthesis module 430 may act as another means of fault or error detection within the CAM module 230.

Receiving input of Y(k), Y_(C)(k), X_(PDot)(k), and/or ErrSlvr(k), the estimate state module 440 may use said inputs to determine the next pass value of CAM state vector X(k). The estimate state module 440 may scale and correct solver state error vector, select solver gain scheduling parameters, calculate solver state gains, calculate scale, and correct the corrector state error vector, integrate state derivatives, apply state integrator range limits, reset state integrators during initialization, detect saturated state integrators, and/or detect internal errors indicated by unreasonably large synthesized values.

The estimate state module 440 may receive input of the effector vector (U_(E)), the synthesized parameters vector (Y), the on-board corrector state vector (X_(C)), the physics state vector (X_(P)), the solver state vector (X_(s)), the solver errors vector (errSlver), and the physics state derivatives vector (X_(PDot)). The estimate state module 440 may output updated versions of the on-board corrector state vector (X_(C_ESM)), the physics state vector (X_(P_ESM)), and the solver state vector (X_(S_ESM)). These vectors are the analyzed state vectors from the current iteration of the open loop model 410. In general, the estimate state module 440 seeks time-advanced vectors that minimize error for the model output. The model state estimator 440 may also minimize physics state derivatives.

In order to achieve the goals of the estimate state module 440, the estimate state module 440 may predict (or estimate) future model state values, with some degree of accuracy, in order to reduce the magnitude of error vectors. In particular, the estimate state module 440 may be able to accurately formulate a feedback and employ a gain design scheme that addresses cross-dependencies among the different elements of the state vectors and error vectors.

The estimate state module 440 may employ two approaches to gain design, depending upon the strength of the cross-dependencies. In the single-input, single-output approach (SISO), distinct elements of error vectors are closely associated with distinct elements of the state vector and not substantially cross-correlated. Thus, gain formulation can be decoupled for each individual element. In the multiple-input, multiple output (MIMO) approach, the error vector and state vector are substantially cross-correlated with one another, and the problem is not decoupled. The MIMO approach may further be divided into control and observer formulations.

However, the open loop model 410 utilizes non-linear approximations, such that the particular form of gain varies as a nonlinear function of the input vectors to the estimate state module 440 (e.g., vectors Y, U_(E), etc.). In addition, the gain design process may be repeated over a rich set of inputs, representing the full operational envelope of each individual vector element. The elements of the gain are then determined on the basis of model fidelity, control response and stability over the entire operational range. While gain parameters may be determined off-line and pre-programmed in the estimate state module 440 for a variety of scenarios, the present estimate state module 440 employs online linearization and gain calculation to determine gain parameters at timesteps of the CAM object 230.

Turning now to FIG. 7, an example estimate state module 440 is shown in greater detail. An aim of the estimate state module 440 is to drive the corrector error (errC) and solver error (errSlvr) as close to zero as possible. In so doing, the estimate state module 440 receives input from the open loop model 410, including error, and multiplies the error by an estimator gain to obtain state derivatives. The specific gain by which the errors are multiplied is determined using a multidimensional (n-D) gain base point system interpolation 441.

Gain design comprises a number of steps, including, but not limited to: selecting an optimum set of scheduling parameters, defining a data set, synthesizing an optimum base point system, linearization, scaling, and gain sizing. Optimum scheduling parameters may be determined on the basis of unique criteria including base point field coverage, independence, surface smoothness, variable validity, and gain margin statistics. The criteria are evaluated both with respect to the gain parameters themselves and with respect to vector derivatives. Scheduling parameters may include elements of external input vectors, state vectors, output vectors, error vectors and the like.

The plot 50 of FIG. 8 illustrates a bi-variant or two-dimensional approach with two scheduling parameters P₁ and P₂, plotted against each other with the first parameter P₁ on the horizontal axis and the second parameter P₂ on the vertical axis. While the example plot 50 only includes two scheduling parameters, the n-D gain base point system interpolation 441 is not limited to two scheduling parameters; rather the system interpolation 441 may include “n” scheduling parameters for an “n-dimensional” approach. Typically, engineering complexity requires at least two scheduling parameters and for the sake of simplicity and example, a two-dimensional plot is shown.

Scheduling parameters P₁ and P₂ are defined for steady state and transient operational conditions spanning a rich data set of design, off-design, and failure conditions. A linear space representation is obtained at each design point, applying various linearization techniques to accurately predict system behavior in the vicinity of each data point 51 (triangles). Linearization is verified by a step response time domain analysis and/or any other validation process, which is applied to a subset of the design data points.

Linear system variables are scaled by maximum values, to improve accuracy of numerical tensor operations during gain design. Correcting linear system variables improves effectiveness of the estimate state module 440 when operating at non-standard boundary conditions. Scaled and corrected linear models are then used in the gain design process.

The linear representation and gain size may vary between points of the design data set. An optimum base point system is synthesized where each base point 52 (circles) contains a complete specification of the linear system. The operating envelope is covered by a finite number of base points, each defined with scheduling parameters P₁ and P₂, or another set of scheduling parameters. This results in a grid of base points 52, forming a base point field that spans the relevant scheduling parameter range.

A linear state space representation may be synthesized at each base point 52 from a finite number of design data points located in close proximity to the base point. Proximity is defined by base point cloud 53, with radius r selected as an optimization parameter. One approach utilizes a weighted average of data points 51 located within base point cloud 53. The weighting factor is defined, for example, as a function of the non-dimensional distance between base point 52 and data point 51.

The gain may be sized for each system of base points, and the estimate state module obtains values of the gain at each time step by using n-D gain base point system interpolation 441. That is, the field of optimized base points provides a “lookup table” from which the gains can be interpolated so that elements can be rapidly and effectively determined as functions of the n scheduling parameters.

Returning now to FIG. 7, the estimator gain determined by the n-D gain base point system interpolation 441 is then used to determine state derivatives and aid in driving errors to, or close to, zero. Using the errors and input from the open loop model 410, the estimate state module 440 then uses numerical integration 448 to integrate the state derivatives to determine the estimate state module 440 model output. The model output may include the next-step values for corrector and solver state vectors.

Turning now to FIG. 9, an alternative embodiment of the estimate state module 440 is shown. The example estimate state module 440 of FIG. 9 is a fixed structure with a data-configurable architecture for supporting multiple estimator configurations. The estimate state module 440 receives input from the open loop model 410, including error, and scales/corrects the error input using scaling/correcting factors 442. The estimate state module 440 of FIG. 9 includes multiple legs to estimate the gain for predefined subsets of state vectors contained in the input vectors, ultimately producing the estimated state output. The first leg 443 selects the groups of the vectors for gain determination, interpolation, and assignment. For example, at leg 443, designated groups (e.g., 1 and 2) of the corrector vector are selected, second groups (e.g., 3, 4, and 5) of corrector vectors are selected, solver states vectors are selected, and physics derivative vectors are selected for further processing.

At leg 444, specified gains are applied to each selected group of error vectors. For example, specific gains may be formulated and applied to corrector error vectors X_(C12), corrector error vectors X_(C345), solver error vector X_(S), and physics state derivative vector X_(PDot). After gain is applied, the vectors may be unscaled/uncorrected at 445. Once unscaled/uncorrected, the estimate state module 440 may perform numerical integration of state derivatives at leg 448 to determine estimated states that will drive errors towards zero. After numerical integration, the estimate state module 440 may assign values to the output vectors of the estimate state module 440 at leg 446. For example, leg 446 may produce output vectors X_(C_ESM), X_(S_ESM), and X_(P_ESM).

Other example structures for estimate state modules 440 may be used in application of the CAM object 230. For example, the estimate state module 440 may use multi-variable optimization to select a base point system, including scheduling parameters. Further, the estimate state module 440 may employ averaging algorithms, which may include, but are not limited to including, factors such as relative importance and certainty. Theses implementations and other base point systems may achieve favorable tradeoff between EPOS 110 dynamic fidelity and numerical stability and may enable the control system 100 to maximize performance at lower development and production cost.

The output of the estimate state module 440 is received by the CAM output object 240, which is illustrated in FIG. 6. The CAM output object 240 may include, but is not limited to including, a vector unpacker 910, a temperatures valuator 920, a pressures valuator 930, a flows valuator 940, a sensor temperatures valuator 950, an other output synthesizer 960, and a status indicator 970. The vector unpacker 910 may receive input of the synthesized parameters vector (Y). The vector unpacker 910 may output the unpacked Y vector to other elements of the output conditioning module. The status indicator 970 may receive input of the operating mode vector (OpMode). Further, the output conditioning module 240 is not limited to including the above mentioned elements, but rather, output conditioning module 240 may omit elements and/or include other elements.

The temperatures valuator 920 may process temperature related values of the synthesized parameters vector (Y). This may include performing unit conversions, implementing test adders, performing non-linear interpolation between temperature values during the starting functions of the apparatus 130, and/or obtaining temperature values from default tables as backup if/when needed. The temperatures valuator 920 is not limited to functioning in the above mentioned manner, rather, the temperatures valuator 920 may omit any of the listed functions and/or add additional functions related to processing temperature data of the synthesized parameters vector (Y).

The pressures valuator 930 may process pressure related values of the synthesized parameters vector (Y). This may include performing unit conversions, implementing test adders, performing non-linear interpolation between pressure values during the starting functions of the apparatus 130, and/or obtaining pressure values from default tables as backup if/when needed. The pressures valuator 930 is not limited to functioning in the above mentioned manner, rather, the pressures valuator 930 may omit any of the listed functions and/or add additional functions related to processing pressure data of the synthesized parameters vector (Y).

The flows valuator 940 may process fuel flow related values of the synthesized parameters vector (Y). This may include performing unit conversions, implementing test adders, performing non-linear interpolation between fuel flow values during the starting functions of the apparatus 130, and/or obtaining fuel flow values from default tables as backup if/when needed. The flows valuator 940 is not limited to functioning in the above mentioned manner, rather, the flows valuator 940 may omit any of the listed functions and/or add additional functions related to processing fuel flow data of the synthesized parameters vector (Y).

The sensor temperatures valuator 950 may process sensor temperature related values of the synthesized parameters vector (Y). This may include performing unit conversions, implementing test adders, performing non-linear interpolation between temperature values during the starting functions of the apparatus 130, and/or obtaining sensor temperature values from default tables as backup if/when needed. The sensor temperatures valuator 950 is not limited to functioning in the above mentioned manner, rather, the sensor temperatures valuator 950 may omit any of the listed functions and/or add additional functions related to processing sensor temperature data of the synthesized parameters vector (Y).

The other output synthesizer 960 may process other output data of the synthesized parameters vector (Y) not processed by the temperatures valuator 920, the pressures valuator 930, the flows valuator 940, the sensors and/or the temperatures valuator 950. This may include performing unit conversions, implementing test adders, performing non-linear interpolation between temperature values during the starting functions of the apparatus 130, and/or obtaining other output values from default tables as backup if/when needed. The flows valuator 940 is not limited to functioning in the above mentioned manner, rather, the flows valuator 940 may omit any of the listed functions and/or add additional functions related to processing other output data of the synthesized parameters vector (Y).

The status indicator 970 may receive input from the operating mode vector (OpMode). Using this input, the status indicator 970 may generate and provide a status indication of the operating status of the CAM module 230 for use in any downstream logic devices.

While an example manner of implementing the EPOS 110 of FIG. 1 has been illustrated in FIGS. 2-9, one or more elements, processes, and/or devices illustrated in FIGS. 2-9 may be combined, divided, rearranged, omitted, eliminated and/or implemented in any other way. Further, the example elements of FIGS. 1-9 could be implemented by one or more circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the apparatus or system claims of this patent are read to cover a purely software and/or firmware implementation, at least one of the example elements are hereby expressly defined to include a tangible computer readable medium such as a memory, DVD, CD, Blu-ray, etc. storing the software and/or firmware. Further still, the example embodiments of have been illustrated in may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in FIGS. 1-6, and/or may include more than one of any or all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions are shown in FIGS. 10 and 11. In these examples, the machine readable instructions comprise a program for execution by a processor such as the processor such as the processor 1210 shown in the example computer 1200 discussed below in connection with FIG. 9. The program may be embodied in software stored on a tangible computer readable medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor 1210, but the entire program and/or parts thereof could alternatively be executed by a device other than the processor 1210 and/or embodied in firmware or dedicated hardware. Further, although the example programs are described with reference to the flowcharts illustrated in FIGS. 7 and 8, many other methods of implementing embodiments of the present disclosure may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined.

With reference to FIG. 10, example machine readable instructions 1000 may be executed to implement the EPOS 110 of FIGS. 1 and/or 2. With reference to FIGS. 1 and/or 2, the example machine readable instructions 1000 begin execution at block 1010 at input vectors are received by the CAM input object 220 and are used by the CAM input object 220 to determine the operating mode (OpMode) of the simulation and to compile the CAM input vectors U_(E) and Y_(Ct) (block 1015). The CAM input vectors are then used by the CAM object 230 to determine synthesized parameters vector Y based on internal physics state module of the CAM module 230 and the external inputs U_(E), Y_(Ct), and OpMode (block 1020). The synthesized parameters vector Y is conditioned by the CAM output object 240 for use by external modules such as, for example, the control law 123 of FIG. 1 (block 1025).

The example machine readable instructions 1100 of FIG. 11 may be executed to implement the CAM object 230 of FIGS. 2 and/or 4. With reference to FIGS. 2 and/or 4, the set state module receives input from and sets the state of the CAM object 230 based upon vectors U_(E), Y_(C)(k), OpMode, and prior physics state vectors generated by the estimate state module 440 in the form of X_(E_ESM)(k−1), X_(C_ESM)(k−1), and X_(P_ESM)(k−1) (block 1110). The open loop model 410 determines synthesized parameters vector Y(k) by processing data contained in U_(E), Y_(C)(k), X_(C)(k−1), X_(S)(k−1), and X_(P)(k−1) using one or more contained cycle synthesis modules, the one or more cycle synthesis modules being a mathematical abstraction(s) of the physics states associated with an element of a cycle of the apparatus 130 (block 1115). The sensing synthesis module 430 receives the synthesized parameters vector Y(k) and detects potential errors in the vector (block 1120). The estimate state module 440 determines the physics state vectors X_(S_ESM)(k), X_(C_ESM)(k), and X_(P_ESM)(k) for the present state (k) (block 1125). The estimate state module 440 outputs the vectors X_(S_ESM)(k), X_(C_ESM)(k), and X_(P_ESM)(k) to the set state module 420 for processing the next sequential state (block 1130). The estimate state module 440 outputs the vector Y(k) for use external to the CAM module 230 (block 1135).

FIG. 12 is a block diagram of an example computer 1200 capable of executing the instructions of FIGS. 7-8 to implement the apparatus of FIGS. 1-6. The computer 1200 can be, for example, a server, a personal computer, or any other type of computing device.

The system 1200 of the instant example includes a processor 1210. For example, the processor 1210 can be implemented by one or more microprocessors or controllers from any desired family or manufacturer.

The processor 1210 includes a local memory 1215 and is in communication with a main memory including a read only memory 1230 and a random access memory 1220 via a bus 1240. The random access memory 1220 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRM) and/or any other type of random access memory device. The read only memory 1230 may be implemented by a hard drive, flash memory and/or any other desired type of memory device.

The computer 1200 also includes an interface circuit 1250. The interface circuit 1230 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.

One or more input devices 1254 are connected to the interface circuit 1250. The input device(s) 1254 permit a user to enter data and commands into the processor 1210. The input device(s) can be implemented by, for example, a keyboard, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. The interface 1250 may operate in conjunction with, in parallel with, or in place of, the operator interface 115 of FIG. 1.

One or more output devices 1258 are also connected to the interface circuit 1250. The output devices 1258 can be implemented by, for example, display devices for associated data (e.g., a liquid crystal display, a cathode ray tube display (CRT), etc.), and/or an actuator operatively associated with a fluid-based engineering system such as gas turbine engines for aviation and power generation, HVAC&R (heating, ventilation, air-conditioning and refrigeration), fuel cells, and other, more generalized fluid processing systems for hydrocarbon extraction, materials processing, and manufacture.

From the foregoing, it can be seen that the technology disclosed herein has industrial applicability in a variety of settings such as, but not limited to, systems and methods for controlling a fluid based engineering system. Example fluid-based engineering systems may include gas turbine engines for aviation and power generation, HVAC&R (heating, ventilation, air-conditioning and refrigeration), fuel cells, and other, more generalized fluid processing systems for hydrocarbon extraction, materials processing, and manufacture. Using the teachings of the present disclosure, compact aero-thermal models of fluid-based engineering systems may be designed to reduce computational load on a system's control device and/or on board processor. The efficiency of such a model may be improved through the use of a series of utilities, the utilities based on mathematical abstractions of physical properties associated with components of the engineering system. This improvement over the prior art may conserve computational efficiency and may improve the accuracy of the control system for fluid based engineering systems.

While the present disclosure has been in reference to a gas turbine engine of an aircraft, one skilled in the art will understand that the teachings herein can be used in other applications as well, as mentioned above. It is therefore intended that the scope of the invention not be limited by the embodiments presented herein as the best mode for carrying out the invention, but that the invention will include all equivalents falling within the spirit and scope of the claims as well. 

What is claimed is:
 1. A control system, comprising: an actuator configured to position a control surface of a control device; and a computer processor configured to execute a control law to control the actuator based on a model output and generate the model output using a model processor, wherein the model processor comprises a plurality of executable instructions to: process a model input vector and set a model operating mode; set dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; generate current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector, wherein a constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; determine an estimated state of the model and reduce error for the model output based on a number of input vectors comprising at least one of a prior state, the current state derivatives, the solver state errors, and the synthesized parameters; determine an estimator gain associated with the current state derivatives and apply the estimator gain to determine the estimated state of the model using a plurality of legs to estimate the gain for predefined subsets of state vectors contained in the input vectors; and process at least the synthesized parameters of the model to determine the model output.
 2. The control system of claim 1, wherein determining the estimator gain comprises employing multidimensional gain base point interpolation.
 3. The control system of claim 2, wherein the multidimensional gain base point interpolation comprises using at least three scheduling parameters.
 4. The control system of claim 1, wherein determining the estimator gain associated with the current state derivatives and applying the estimator gain is achieved by using a fixed structure with a configurable architecture.
 5. The control system of claim 4, wherein the configurable architecture comprises a first leg configured to scale and correct an error input using scaling and correcting factors.
 6. The control system of claim 5, wherein the configurable architecture further comprises: a second leg configured to select groups of vectors from the error input for gain determination; and a third leg configured to determine and apply gain to the groups of vectors.
 7. The control system of claim 6, wherein the configurable architecture further comprises a fourth leg configured to unscale and uncorrect the groups of vectors.
 8. The control system of claim 7, wherein the configurable architecture further comprises a fifth leg configured to assign values to output vectors of the estimated state of the model.
 9. The control system of claim 1, wherein the control device is a gas turbine engine.
 10. A method for controlling a control device, the method comprising: generating, by a computer processor, a model output using a model processor; processing a model input vector and setting a model operating mode; setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; generating current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector, wherein a constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control device and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; determining an estimated state of the model and reducing error for the model output based on a number of input vectors comprising at least one of a prior state model, the current state derivatives, the solver state errors, and the synthesized parameters; determining an estimator gain associated with the current state derivatives and applying the estimator gain to determine the estimated state of the model using a plurality of legs to estimate the gain for predefined subsets of state vectors contained in the input vectors; processing at least the synthesized parameters of the model to determine the model output; controlling an actuator associated with the control device as a function of the model output using a control law; and positioning a control surface of the control device using the actuator.
 11. The method of claim 10, wherein determining the estimator gain comprises employing multidimensional gain base point interpolation.
 12. The method of claim 11, wherein the multidimensional gain base point interpolation includes using at least three scheduling parameters.
 13. The method of claim 10, wherein determining the estimator gain associated with the current state derivatives and applying the estimator gain is achieved by using a fixed structure with a configurable architecture.
 14. The method of claim 13, wherein the configurable architecture comprises a first leg configured to scale and correct an error input using scaling and correcting factors.
 15. The method of claim 14, wherein the configurable architecture further comprises: a second leg configured to select groups of vectors from the error input for gain determination; a third leg configured to determine and apply gain to the groups of vectors; and a fourth leg configured to unscale and uncorrect the groups of vectors.
 16. A gas turbine engine comprising: a fan; a compressor section downstream of the fan; a combustor section downstream of the compressor section; a turbine section downstream of the combustor section; an actuator configured to position a control surface of the gas turbine engine; and a computer processor configured to execute a control law to control the actuator as a function of a model output and generate the model output using a model processor, wherein the model processor comprises a plurality of executable instructions: process a model input vector and set a model operating mode; set dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode; generate current state derivatives, solver state errors, and synthesized parameters as a function of the dynamic states and the model input vector, wherein a constraint on the current state derivatives and solver state errors is based on a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the gas turbine engine and comprising a series of utilities, the utilities based on mathematical abstractions of physical laws that govern behavior of the component; determine an estimated state of the model and reduce error for the model output based on a number of input vectors comprising at least one of a prior state, the current state derivatives, the solver state errors, and the synthesized parameters; determine an estimator gain associated with the current state derivatives and apply the estimator gain to determine the estimated state of the model using a plurality of legs to estimate the gain for predefined subsets of state vectors contained in the input vectors; and process at least the synthesized parameters of the model to determine the model output.
 17. The gas turbine engine of claim 16, wherein determining the estimator gain comprises employing multidimensional gain base point interpolation.
 18. The gas turbine engine of claim 17, wherein the multidimensional gain base point interpolation comprises using at least three scheduling parameters.
 19. The gas turbine engine of claim 16, wherein determining the estimator gain associated with the current state derivatives and applying the estimator gain is achieved by using a fixed structure with a configurable architecture.
 20. The gas turbine engine of claim 19, wherein the configurable architecture comprises: a first leg configured to select groups of vectors from the error input for gain determination; a second leg configured to determine and apply gain to the groups of vectors; a third leg configured to determine and apply gain to the groups of vectors; and a fourth leg configured to unscale and uncorrect the groups of vectors. 